OpenRaiser/Intern-Atlas huggingface dataset:
Injecting Salesperson's Dialogue Strategies in Large Language Models with Chain-of-Thought Reasoning
Venue: arXiv cs.CL 2024
Year: 2024

Recent research in dialogue systems and corpora has focused on two main categories: task-oriented (TOD) and open-domain (chit-chat) dialogues. TOD systems help users accomplish specific tasks, while open-domain systems aim to create engaging conversations. However, in real-world scenarios, user intents are often revealed during interactions. A recent study introduced SalesBot, which simulates dialogues transitioning from chit-chat to task-oriented scenarios to train sales agents. Unfortunately, the initial data lacked smooth transitions and coherent long-turn dialogues, resulting in poor naturalness in sales-customer interactions. To address these issues, this paper presents SalesBot 2.0, an improved dataset. It leverages commonsense knowledge from large language models (LLMs) through strategic prompting. Additionally, we introduce a novel model called SalesAgent, trained on salesperson's interactions, using chain-of-thought (CoT) reasoning. This model excels in transitioning topics, understanding user intents, and selecting appropriate strategies. Experiments using diverse user simulations validate the effectiveness of our method in controlling dialogue strategies in LLMs. Furthermore, SalesBot 2.0 enhances coherence and reduces aggression, facilitating better model learning for sales-customer interactions.
Assessing Quality Metrics for Neural Reality Gap Input Mitigation in Autonomous Driving Testing
Venue: arXiv cs.SE 2024
Year: 2024

Simulation-based testing of automated driving systems (ADS) is the industry standard, being a controlled, safe, and cost-effective alternative to real-world testing. Despite these advantages, virtual simulations often fail to accurately replicate real-world conditions like image fidelity, texture representation, and environmental accuracy. This can lead to significant differences in ADS behavior between simulated and real-world domains, a phenomenon known as the sim2real gap. Researchers have used Image-to-Image (I2I) neural translation to mitigate the sim2real gap, enhancing the realism of simulated environments by transforming synthetic data into more authentic representations of real-world conditions. However, while promising, these techniques may potentially introduce artifacts, distortions, or inconsistencies in the generated data that can affect the effectiveness of ADS testing. In our empirical study, we investigated how the quality of image-to-image (I2I) techniques influences the mitigation of the sim2real gap, using a set of established metrics from the literature. We evaluated two popular generative I2I architectures, pix2pix, and CycleGAN, across two ADS perception tasks at a model level, namely vehicle detection and end-to-end lane keeping, using paired simulated and real-world datasets. Our findings reveal that the effectiveness of I2I architectures varies across different ADS tasks, and existing evaluation metrics do not consistently align with the ADS behavior. Thus, we conducted task-specific fine-tuning of perception metrics, which yielded a stronger correlation. Our findings indicate that a perception metric that incorporates semantic elements, tailored to each task, can facilitate selecting the most appropriate I2I technique for a reliable assessment of the sim2real gap mitigation.
CSTalk: Correlation Supervised Speech-driven 3D Emotional Facial Animation Generation
Venue: arXiv cs.CV 2024
Year: 2024

Speech-driven 3D facial animation technology has been developed for years, but its practical application still lacks expectations. The main challenges lie in data limitations, lip alignment, and the naturalness of facial expressions. Although lip alignment has seen many related studies, existing methods struggle to synthesize natural and realistic expressions, resulting in a mechanical and stiff appearance of facial animations. Even with some research extracting emotional features from speech, the randomness of facial movements limits the effective expression of emotions. To address this issue, this paper proposes a method called CSTalk (Correlation Supervised) that models the correlations among different regions of facial movements and supervises the training of the generative model to generate realistic expressions that conform to human facial motion patterns. To generate more intricate animations, we employ a rich set of control parameters based on the metahuman character model and capture a dataset for five different emotions. We train a generative network using an autoencoder structure and input an emotion embedding vector to achieve the generation of user-control expressions. Experimental results demonstrate that our method outperforms existing state-of-the-art methods.
Differentiable Geodesic Distance for Intrinsic Minimization on Triangle Meshes
Venue: arXiv cs.GR 2024
Year: 2024

Computing intrinsic distances on discrete surfaces is at the heart of many minimization problems in geometry processing and beyond. Solving these problems is extremely challenging as it demands the computation of on-surface distances along with their derivatives. We present a novel approach for intrinsic minimization of distance-based objectives defined on triangle meshes. Using a variational formulation of shortest-path geodesics, we compute first and second-order distance derivatives based on the implicit function theorem, thus opening the door to efficient Newton-type minimization solvers. We demonstrate our differentiable geodesic distance framework on a wide range of examples, including geodesic networks and membranes on surfaces of arbitrary genus, two-way coupling between hosting surface and embedded system, differentiable geodesic Voronoi diagrams, and efficient computation of Karcher means on complex shapes. Our analysis shows that second-order descent methods based on our differentiable geodesics outperform existing first-order and quasi-Newton methods by large margins.
On the Evaluation of Procedural Level Generation Systems
Venue: arXiv cs.HC 2024
Year: 2024

The evaluation of procedural content generation (PCG) systems for generating video game levels is a complex and contested topic. Ideally, the field would have access to robust, generalisable and widely accepted evaluation approaches that can be used to compare novel PCG systems to prior work, but consensus on how to evaluate novel systems is currently limited. We argue that the field can benefit from a structured analysis of how procedural level generation systems can be evaluated, and how these techniques are currently used by researchers. This analysis can then be used to both inform on the current state of affairs, and to provide data to justify changes to this practice. This work aims to provide this by first developing a novel taxonomy of PCG evaluation approaches, and then presenting the results of a survey of recent work in the field through the lens of this taxonomy. The results of this survey highlight several important weaknesses in current practice which we argue could be substantially mitigated by 1) promoting use of evaluation free system descriptions where appropriate, 2) promoting the development of diverse research frameworks, 3) promoting reuse of code and methodology wherever possible.
Convergence Properties of Score-Based Models for Linear Inverse Problems Using Graduated Optimisation
Venue: arXiv cs.LG 2024
Year: 2024

The incorporation of generative models as regularisers within variational formulations for inverse problems has proven effective across numerous image reconstruction tasks. However, the resulting optimisation problem is often non-convex and challenging to solve. In this work, we show that score-based generative models (SGMs) can be used in a graduated optimisation framework to solve inverse problems. We show that the resulting graduated non-convexity flow converge to stationary points of the original problem and provide a numerical convergence analysis of a 2D toy example. We further provide experiments on computed tomography image reconstruction, where we show that this framework is able to recover high-quality images, independent of the initial value. The experiments highlight the potential of using SGMs in graduated optimisation frameworks. The source code is publicly available on GitHub.
Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots
Venue: arXiv cs.LG 2024
Year: 2024

The adoption of artificial intelligence (AI) across industries has led to the widespread use of complex black-box models and interpretation tools for decision making. This paper proposes an adversarial framework to uncover the vulnerability of permutation-based interpretation methods for machine learning tasks, with a particular focus on partial dependence (PD) plots. This adversarial framework modifies the original black box model to manipulate its predictions for instances in the extrapolation domain. As a result, it produces deceptive PD plots that can conceal discriminatory behaviors while preserving most of the original model's predictions. This framework can produce multiple fooled PD plots via a single model. By using real-world datasets including an auto insurance claims dataset and COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) dataset, our results show that it is possible to intentionally hide the discriminatory behavior of a predictor and make the black-box model appear neutral through interpretation tools like PD plots while retaining almost all the predictions of the original black-box model. Managerial insights for regulators and practitioners are provided based on the findings.
Innovative Integration of Visual Foundation Model with a Robotic Arm on a Mobile Platform
Venue: arXiv cs.RO 2024
Year: 2024

In the rapidly advancing field of robotics, the fusion of state-of-the-art visual technologies with mobile robotic arms has emerged as a critical integration. This paper introduces a novel system that combines the Segment Anything model (SAM) -- a transformer-based visual foundation model -- with a robotic arm on a mobile platform. The design of integrating a depth camera on the robotic arm's end-effector ensures continuous object tracking, significantly mitigating environmental uncertainties. By deploying on a mobile platform, our grasping system has an enhanced mobility, playing a key role in dynamic environments where adaptability are critical. This synthesis enables dynamic object segmentation, tracking, and grasping. It also elevates user interaction, allowing the robot to intuitively respond to various modalities such as clicks, drawings, or voice commands, beyond traditional robotic systems. Empirical assessments in both simulated and real-world demonstrate the system's capabilities. This configuration opens avenues for wide-ranging applications, from industrial settings, agriculture, and household tasks, to specialized assignments and beyond.
The Constant in HATE: Analyzing Toxicity in Reddit across Topics and Languages
Venue: arXiv cs.CL 2024
Year: 2024

Toxic language remains an ongoing challenge on social media platforms, presenting significant issues for users and communities. This paper provides a cross-topic and cross-lingual analysis of toxicity in Reddit conversations. We collect 1.5 million comment threads from 481 communities in six languages: English, German, Spanish, Turkish,Arabic, and Dutch, covering 80 topics such as Culture, Politics, and News. We thoroughly analyze how toxicity spikes within different communities in relation to specific topics. We observe consistent patterns of increased toxicity across languages for certain topics, while also noting significant variations within specific language communities.
Desirability and social rankings
Venue: arXiv cs.GT 2024
Year: 2024

In coalitional games, a player $i$ is regarded as strictly more desirable than player $j$ if substituting $j$ with $i$ within any coalition leads to a strict augmentation in the value of certain coalitions, while preserving the value of the others. We adopt a property-driven approach to 'integrate' the notion of the desirability relation into a total relation by establishing sets of independent axioms leading to the characterization of solutionconcepts from the related literature. We focus on social ranking solutions consistent with the desirability relation and propose complementary sets of properties for the axiomatic characterization of five existing solutions: Ceteris Paribus (CP-)majority, lexicographic excellence (lex-cel), dual-lex, $L^{(1)}$ solution and its dual version $L^{(1)}_{*}$ . These characterizations reveal additional similarities among the five solutions and emphasize the essential characteristics that should be taken into account when selecting a social ranking. A practical scenario involving a bicameral legislature is studied.
Saliency Suppressed, Semantics Surfaced: Visual Transformations in Neural Networks and the Brain
Venue: arXiv cs.CV 2024
Year: 2024

Deep learning algorithms lack human-interpretable accounts of how they transform raw visual input into a robust semantic understanding, which impedes comparisons between different architectures, training objectives, and the human brain. In this work, we take inspiration from neuroscience and employ representational approaches to shed light on how neural networks encode information at low (visual saliency) and high (semantic similarity) levels of abstraction. Moreover, we introduce a custom image dataset where we systematically manipulate salient and semantic information. We find that ResNets are more sensitive to saliency information than ViTs, when trained with object classification objectives. We uncover that networks suppress saliency in early layers, a process enhanced by natural language supervision (CLIP) in ResNets. CLIP also enhances semantic encoding in both architectures. Finally, we show that semantic encoding is a key factor in aligning AI with human visual perception, while saliency suppression is a non-brain-like strategy.
Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models
Venue: arXiv cs.CL 2024
Year: 2024

As Large Language Models (LLMs) have become more advanced, they have outpaced our abilities to accurately evaluate their quality. Not only is finding data to adequately probe particular model properties difficult, but evaluating the correctness of a model's freeform generation alone is a challenge. To address this, many evaluations now rely on using LLMs themselves as judges to score the quality of outputs from other LLMs. Evaluations most commonly use a single large model like GPT4. While this method has grown in popularity, it is costly, has been shown to introduce intramodel bias, and in this work, we find that very large models are often unnecessary. We propose instead to evaluate models using a Panel of LLm evaluators (PoLL). Across three distinct judge settings and spanning six different datasets, we find that using a PoLL composed of a larger number of smaller models outperforms a single large judge, exhibits less intra-model bias due to its composition of disjoint model families, and does so while being over seven times less expensive.
Belt and Braces: When Federated Learning Meets Differential Privacy
Venue: arXiv cs.CR 2024
Year: 2024

Federated learning (FL) has great potential for large-scale machine learning (ML) without exposing raw data.Differential privacy (DP) is the de facto standard of privacy protection with provable guarantees.Advances in ML suggest that DP would be a perfect fit for FL with comprehensive privacy preservation. Hence, extensive efforts have been devoted to achieving practically usable FL with DP, which however is still challenging.Practitioners often not only are not fully aware of its development and categorization, but also face a hard choice between privacy and utility. Therefore, it calls for a holistic review of current advances and an investigation on the challenges and opportunities for highly usable FL systems with a DP guarantee. In this article, we first introduce the primary concepts of FL and DP, and highlight the benefits of integration. We then review the current developments by categorizing different paradigms and notions. Aiming at usable FL with DP, we present the optimization principles to seek a better tradeoff between model utility and privacy loss. Finally, we discuss future challenges in the emergent areas and relevant research topics.
Sparse Sampling in Fractional Fourier Domain: Recovery Guarantees and Cramér-Rao Bounds
Venue: arXiv cs.IT 2024
Year: 2024

Sampling theory in fractional Fourier Transform (FrFT) domain has been studied extensively in the last decades. This interest stems from the ability of the FrFT to generalize the traditional Fourier Transform, broadening the traditional concept of bandwidth and accommodating a wider range of functions that may not be bandlimited in the Fourier sense. Beyond bandlimited functions, sampling and recovery of sparse signals has also been studied in the FrFT domain. Existing methods for sparse recovery typically operate in the transform domain, capitalizing on the spectral features of spikes in the FrFT domain. Our paper contributes two new theoretical advancements in this area. First, we introduce a novel time-domain sparse recovery method that avoids the typical bottlenecks of transform domain methods, such as spectral leakage. This method is backed by a sparse sampling theorem applicable to arbitrary FrFT-bandlimited kernels and is validated through a hardware experiment. Second, we present Cramér-Rao Bounds for the sparse sampling problem, addressing a gap in existing literature.
A Comprehensive Rubric for Annotating Pathological Speech
Venue: arXiv cs.CL 2024
Year: 2024

Rubrics are a commonly used tool for labeling voice corpora in speech quality assessment, although their application in the context of pathological speech remains relatively limited. In this study, we introduce a comprehensive rubric based on various dimensions of speech quality, including phonetics, fluency, and prosody. The objective is to establish standardized criteria for identifying errors within the speech of individuals with Down syndrome, thereby enabling the development of automated assessment systems. To achieve this objective, we utilized the Prautocal corpus. To assess the quality of annotations using our rubric, two experiments were conducted, focusing on phonetics and fluency. For phonetic evaluation, we employed the Goodness of Pronunciation (GoP) metric, utilizing automatic segmentation systems and correlating the results with evaluations conducted by a specialized speech therapist. While the obtained correlation values were not notably high, a positive trend was observed. In terms of fluency assessment, deep learning models like wav2vec were used to extract audio features, and we employed an SVM classifier trained on a corpus focused on identifying fluency issues to categorize Prautocal corpus samples. The outcomes highlight the complexities of evaluating such phenomena, with variability depending on the specific type of disfluency detected.
Spivavtor: An Instruction Tuned Ukrainian Text Editing Model
Venue: arXiv cs.CL 2024
Year: 2024

We introduce Spivavtor, a dataset, and instruction-tuned models for text editing focused on the Ukrainian language. Spivavtor is the Ukrainian-focused adaptation of the English-only CoEdIT model. Similar to CoEdIT, Spivavtor performs text editing tasks by following instructions in Ukrainian. This paper describes the details of the Spivavtor-Instruct dataset and Spivavtor models. We evaluate Spivavtor on a variety of text editing tasks in Ukrainian, such as Grammatical Error Correction (GEC), Text Simplification, Coherence, and Paraphrasing, and demonstrate its superior performance on all of them. We publicly release our best-performing models and data as resources to the community to advance further research in this space.
Hide and Seek: How Does Watermarking Impact Face Recognition?
Venue: arXiv cs.CV 2024
Year: 2024

The recent progress in generative models has revolutionized the synthesis of highly realistic images, including face images. This technological development has undoubtedly helped face recognition, such as training data augmentation for higher recognition accuracy and data privacy. However, it has also introduced novel challenges concerning the responsible use and proper attribution of computer generated images. We investigate the impact of digital watermarking, a technique for embedding ownership signatures into images, on the effectiveness of face recognition models. We propose a comprehensive pipeline that integrates face image generation, watermarking, and face recognition to systematically examine this question. The proposed watermarking scheme, based on an encoder-decoder architecture, successfully embeds and recovers signatures from both real and synthetic face images while preserving their visual fidelity. Through extensive experiments, we unveil that while watermarking enables robust image attribution, it results in a slight decline in face recognition accuracy, particularly evident for face images with challenging poses and expressions. Additionally, we find that directly training face recognition models on watermarked images offers only a limited alleviation of this performance decline. Our findings underscore the intricate trade off between watermarking and face recognition accuracy. This work represents a pivotal step towards the responsible utilization of generative models in face recognition and serves to initiate discussions regarding the broader implications of watermarking in biometrics.
Swin2-MoSE: A New Single Image Super-Resolution Model for Remote Sensing
Venue: arXiv cs.CV 2024
Year: 2024

Due to the limitations of current optical and sensor technologies and the high cost of updating them, the spectral and spatial resolution of satellites may not always meet desired requirements. For these reasons, Remote-Sensing Single-Image Super-Resolution (RS-SISR) techniques have gained significant interest. In this paper, we propose Swin2-MoSE model, an enhanced version of Swin2SR. Our model introduces MoE-SM, an enhanced Mixture-of-Experts (MoE) to replace the Feed-Forward inside all Transformer block. MoE-SM is designed with Smart-Merger, and new layer for merging the output of individual experts, and with a new way to split the work between experts, defining a new per-example strategy instead of the commonly used per-token one. Furthermore, we analyze how positional encodings interact with each other, demonstrating that per-channel bias and per-head bias can positively cooperate. Finally, we propose to use a combination of Normalized-Cross-Correlation (NCC) and Structural Similarity Index Measure (SSIM) losses, to avoid typical MSE loss limitations. Experimental results demonstrate that Swin2-MoSE outperforms any Swin derived models by up to 0.377 - 0.958 dB (PSNR) on task of 2x, 3x and 4x resolution-upscaling (Sen2Venus and OLI2MSI datasets). It also outperforms SOTA models by a good margin, proving to be competitive and with excellent potential, especially for complex tasks. Additionally, an analysis of computational costs is also performed. Finally, we show the efficacy of Swin2-MoSE, applying it to a semantic segmentation task (SeasoNet dataset). Code and pretrained are available on https://github.com/IMPLabUniPr/swin2-mose/tree/official_code